import gradio as gr
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from prophet.serialize import model_from_json
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.stattools import adfuller
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import LSTM, Dense, Dropout
import pickle
import warnings
warnings.filterwarnings('ignore')

np.random.seed(42)
tf.random.set_seed(42)

# ============================================================================
# DATA LOADING
# ============================================================================

def load_stock_data():
    """Load AAPL stock data from CSV file"""
    try:
        df = pd.read_csv('AAPL_stock_data.csv', parse_dates=['Date'])
        df = df.sort_values('Date').reset_index(drop=True)
        df['Returns'] = df['Close'].pct_change()
        
        print(f"Loaded {len(df)} records from AAPL_stock_data.csv")
        print(f"Date Range: {df['Date'].min()} to {df['Date'].max()}")
        return df
    except Exception as e:
        print(f"Error loading CSV: {e}")
        raise Exception("Please upload AAPL_stock_data.csv file")

# ============================================================================
# METRICS CALCULATION
# ============================================================================

def calculate_metrics(y_true, y_pred):
    """Calculate evaluation metrics"""
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
    mae = mean_absolute_error(y_true, y_pred)
    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100
    r2 = r2_score(y_true, y_pred)
    return rmse, mae, mape, r2

# ============================================================================
# STATIONARITY TEST
# ============================================================================

def test_stationarity(timeseries):
    """Perform Augmented Dickey-Fuller test"""
    result = adfuller(timeseries.dropna(), autolag='AIC')
    return {
        'statistic': result[0],
        'pvalue': result[1],
        'is_stationary': result[1] <= 0.05,
        'critical_values': result[4]
    }

# ============================================================================
# MODEL 1: ARIMA
# ============================================================================

def load_arima_model():
    """Load pre-trained ARIMA model"""
    try:
        with open('models/arima_model.pkl', 'rb') as f:
            model = pickle.load(f)
        print("ARIMA model loaded successfully")
        return model
    except Exception as e:
        print(f"ARIMA Load Error: {e}")
        return None

# ============================================================================
# MODEL 2: PROPHET
# ============================================================================

def load_prophet_model():
    """Load pre-trained Prophet model"""
    try:
        # Load the model from JSON
        with open('models/prophet_model.json', 'r') as f:
            model = model_from_json(f.read())
        print("Prophet model loaded successfully")
        return model
    except Exception as e:
        print(f"Prophet Load Error: {e}")
        return None

# ============================================================================
# MODEL 3: LSTM
# ============================================================================

def load_lstm_model():
    """Load pre-trained LSTM model and scaler"""
    try:
        # Load the model
        lstm_model = load_model('models/lstm_model.h5')
        
        # Load the scaler
        with open('models/lstm_scaler.pkl', 'rb') as f:
            scaler = pickle.load(f)
        
        print("LSTM model and scaler loaded successfully")
        return lstm_model, scaler
    except Exception as e:
        print(f"LSTM Load Error: {e}")
        return None, None

# ============================================================================
# EDA FUNCTION
# ============================================================================

def generate_eda():
    """Generate exploratory data analysis"""
    try:
        df = load_stock_data()
        
        # Create subplots
        fig = make_subplots(
            rows=2, cols=2,
            subplot_titles=('Stock Close Price Over Time', 'Trading Volume', 
                          'Daily Returns', 'Returns Distribution'),
            specs=[[{"secondary_y": False}, {"secondary_y": False}],
                   [{"secondary_y": False}, {"secondary_y": False}]]
        )
        
        # Close Price
        fig.add_trace(
            go.Scatter(x=df['Date'], y=df['Close'], mode='lines', 
                      name='Close Price', line=dict(color='blue', width=1.5)),
            row=1, col=1
        )
        
        # Volume
        fig.add_trace(
            go.Bar(x=df['Date'], y=df['Volume'], name='Volume', 
                  marker=dict(color='green', opacity=0.6)),
            row=1, col=2
        )
        
        # Daily Returns
        fig.add_trace(
            go.Scatter(x=df['Date'], y=df['Returns'], mode='lines',
                      name='Returns', line=dict(color='red', width=0.8)),
            row=2, col=1
        )
        fig.add_hline(y=0, line_dash="dash", line_color="black", row=2, col=1)
        
        # Returns Distribution
        fig.add_trace(
            go.Histogram(x=df['Returns'].dropna(), nbinsx=50, 
                        name='Distribution', marker=dict(color='purple', opacity=0.7)),
            row=2, col=2
        )
        
        fig.update_layout(height=800, showlegend=False, template='plotly_white')
        fig.update_xaxes(title_text="Date", row=1, col=1)
        fig.update_xaxes(title_text="Date", row=1, col=2)
        fig.update_xaxes(title_text="Date", row=2, col=1)
        fig.update_xaxes(title_text="Returns", row=2, col=2)
        fig.update_yaxes(title_text="Price ($)", row=1, col=1)
        fig.update_yaxes(title_text="Volume", row=1, col=2)
        fig.update_yaxes(title_text="Returns", row=2, col=1)
        fig.update_yaxes(title_text="Frequency", row=2, col=2)
        
        # Statistics
        stats = f"""
### 📊 Dataset Statistics

**Price Statistics:**
- Mean Close Price: ${df['Close'].mean():.2f}
- Median Close Price: ${df['Close'].median():.2f}
- Min Close Price: ${df['Close'].min():.2f}
- Max Close Price: ${df['Close'].max():.2f}
- Standard Deviation: ${df['Close'].std():.2f}

**Returns Statistics:**
- Mean Daily Return: {df['Returns'].mean()*100:.4f}%
- Volatility (Std): {df['Returns'].std()*100:.4f}%
- Min Return: {df['Returns'].min()*100:.2f}%
- Max Return: {df['Returns'].max()*100:.2f}%

**Dataset Info:**
- Total Records: {len(df)}
- Date Range: {df['Date'].min().strftime('%Y-%m-%d')} to {df['Date'].max().strftime('%Y-%m-%d')}
- Missing Values: {df.isnull().sum().sum()}
        """
        
        # Stationarity test
        stationarity_result = test_stationarity(df['Close'])
        
        stationarity_text = f"""
### 🔬 Stationarity Test (Augmented Dickey-Fuller)

- **ADF Statistic:** {stationarity_result['statistic']:.6f}
- **p-value:** {stationarity_result['pvalue']:.6f}
- **Result:** {'✅ Series is STATIONARY' if stationarity_result['is_stationary'] else '❌ Series is NON-STATIONARY'}

**Critical Values:**
"""
        for key, value in stationarity_result['critical_values'].items():
            stationarity_text += f"- {key}: {value:.3f}\n"
        
        return fig, stats, stationarity_text
        
    except Exception as e:
        error_msg = f"### ❌ Error\n\n{str(e)}"
        return None, error_msg, error_msg

# ============================================================================
# ROLLING WINDOW EVALUATION
# ============================================================================

def rolling_window_evaluation(df, test_size=90):
    """Perform rolling window evaluation for all models"""
    
    print("\n" + "="*70)
    print("RUNNING MODEL EVALUATION")
    print("="*70)
    
    train_data = df[:-test_size].copy()
    test_data = df[-test_size:].copy()
    
    print(f"Training set: {len(train_data)} days")
    print(f"Test set: {len(test_data)} days")
    
    results = {
        'ARIMA': {'predictions': [], 'metrics': {}},
        'LSTM': {'predictions': [], 'metrics': {}},
        'Prophet': {'predictions': [], 'metrics': {}}
    }
    
    # ARIMA
    arima_model = load_arima_model()
    if arima_model:
        arima_forecast = arima_model.forecast(steps=test_size)
        results['ARIMA']['predictions'] = arima_forecast
        rmse, mae, mape, r2 = calculate_metrics(test_data['Close'].values, arima_forecast)
        results['ARIMA']['metrics'] = {'RMSE': rmse, 'MAE': mae, 'MAPE': mape, 'R2': r2}
        print(f"ARIMA - RMSE: {rmse:.2f}, MAE: {mae:.2f}, MAPE: {mape:.2f}%, R2: {r2:.4f}")
    
    # Prophet
    prophet_model = load_prophet_model()
    if prophet_model:
        future = prophet_model.make_future_dataframe(periods=test_size)
        forecast = prophet_model.predict(future)
        prophet_forecast = forecast['yhat'].iloc[-test_size:].values
        results['Prophet']['predictions'] = prophet_forecast
        rmse, mae, mape, r2 = calculate_metrics(test_data['Close'].values, prophet_forecast)
        results['Prophet']['metrics'] = {'RMSE': rmse, 'MAE': mae, 'MAPE': mape, 'R2': r2}
        print(f"Prophet - RMSE: {rmse:.2f}, MAE: {mae:.2f}, MAPE: {mape:.2f}%, R2: {r2:.4f}")
    
    # LSTM
    lstm_model, scaler = load_lstm_model()
    if lstm_model and scaler:
        all_data = df['Close'].values
        test_inputs = all_data[len(train_data) - 60:]  # Using look_back=60 as in original code
        test_inputs = scaler.transform(test_inputs.reshape(-1, 1))
        
        X_test = []
        for i in range(60, len(test_inputs)):
            X_test.append(test_inputs[i-60:i, 0])
        X_test = np.array(X_test)
        X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
        
        lstm_predictions = lstm_model.predict(X_test, verbose=0)
        lstm_predictions = scaler.inverse_transform(lstm_predictions)
        results['LSTM']['predictions'] = lstm_predictions.flatten()
        
        rmse, mae, mape, r2 = calculate_metrics(test_data['Close'].values, lstm_predictions.flatten())
        results['LSTM']['metrics'] = {'RMSE': rmse, 'MAE': mae, 'MAPE': mape, 'R2': r2}
        print(f"LSTM - RMSE: {rmse:.2f}, MAE: {mae:.2f}, MAPE: {mape:.2f}%, R2: {r2:.4f}")
    
    print("="*70 + "\n")
    return results, train_data, test_data

# ============================================================================
# FUTURE PREDICTIONS
# ============================================================================

def predict_future(df, days_to_predict=30):
    """Make future predictions with all three models"""
    
    print("\n" + "="*70)
    print(f"GENERATING {days_to_predict}-DAY PREDICTIONS")
    print("="*70)
    
    full_data = df['Close'].values
    predictions = {}
    
    # ARIMA
    arima_model = load_arima_model()
    if arima_model:
        arima_forecast = arima_model.forecast(steps=days_to_predict)
        predictions['ARIMA'] = arima_forecast
    
    # Prophet
    prophet_model = load_prophet_model()
    if prophet_model:
        future = prophet_model.make_future_dataframe(periods=days_to_predict)
        forecast = prophet_model.predict(future)
        predictions['Prophet'] = forecast['yhat'].iloc[-days_to_predict:].values
    
    # LSTM
    lstm_model, scaler = load_lstm_model()
    if lstm_model and scaler:
        last_sequence = scaler.transform(full_data[-60:].reshape(-1, 1))  # Using look_back=60
        lstm_predictions = []
        
        for _ in range(days_to_predict):
            X_test = last_sequence.reshape((1, 60, 1))
            pred = lstm_model.predict(X_test, verbose=0)
            lstm_predictions.append(pred[0, 0])
            last_sequence = np.append(last_sequence[1:], pred).reshape(-1, 1)
        
        lstm_predictions = scaler.inverse_transform(np.array(lstm_predictions).reshape(-1, 1))
        predictions['LSTM'] = lstm_predictions.flatten()
    
    print("="*70 + "\n")
    return predictions

# ============================================================================
# VISUALIZATION FUNCTIONS
# ============================================================================

def create_comparison_plot(results, train_data, test_data):
    """Create comparison plot for model evaluation"""
    fig = go.Figure()
    
    fig.add_trace(go.Scatter(
        x=train_data['Date'],
        y=train_data['Close'],
        mode='lines',
        name='Training Data',
        line=dict(color='blue', width=1.5)
    ))
    
    fig.add_trace(go.Scatter(
        x=test_data['Date'],
        y=test_data['Close'],
        mode='lines',
        name='Actual Test Data',
        line=dict(color='green', width=3)
    ))
    
    colors = {'ARIMA': 'red', 'LSTM': 'orange', 'Prophet': 'purple'}
    
    for model_name in ['ARIMA', 'LSTM', 'Prophet']:
        if len(results[model_name]['predictions']) > 0:
            fig.add_trace(go.Scatter(
                x=test_data['Date'],
                y=results[model_name]['predictions'],
                mode='lines',
                name=f'{model_name} Prediction',
                line=dict(color=colors[model_name], dash='dash', width=2)
            ))
    
    fig.update_layout(
        title='AAPL Stock Price - Model Comparison (Rolling Window Evaluation)',
        xaxis_title='Date',
        yaxis_title='Stock Price ($)',
        hovermode='x unified',
        height=600,
        template='plotly_white',
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
    )
    
    return fig

def create_future_plot(df, predictions, days_to_predict):
    """Create plot for future predictions"""
    fig = go.Figure()
    
    recent_data = df.tail(180)
    fig.add_trace(go.Scatter(
        x=recent_data['Date'],
        y=recent_data['Close'],
        mode='lines',
        name='Historical Data',
        line=dict(color='blue', width=2)
    ))
    
    last_date = df['Date'].iloc[-1]
    future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=days_to_predict)
    
    colors = {'ARIMA': 'red', 'LSTM': 'orange', 'Prophet': 'purple'}
    
    for model_name, preds in predictions.items():
        fig.add_trace(go.Scatter(
            x=future_dates,
            y=preds,
            mode='lines+markers',
            name=f'{model_name} Forecast',
            line=dict(color=colors[model_name], dash='dash', width=2),
            marker=dict(size=4)
        ))
    
    fig.update_layout(
        title=f'AAPL Stock Price Forecast - Next {days_to_predict} Days',
        xaxis_title='Date',
        yaxis_title='Stock Price ($)',
        hovermode='x unified',
        height=600,
        template='plotly_white',
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
    )
    
    return fig

# ============================================================================
# FORMATTING FUNCTIONS
# ============================================================================

def format_metrics_table(results):
    """Format metrics as markdown table"""
    table = "### 📊 Model Performance Comparison\n\n"
    table += "| Model | RMSE | MAE | MAPE (%) | R² Score |\n"
    table += "|-------|------|-----|----------|----------|\n"
    
    for model_name in ['ARIMA', 'LSTM', 'Prophet']:
        metrics = results[model_name]['metrics']
        if metrics:
            table += f"| **{model_name}** | {metrics['RMSE']:.2f} | {metrics['MAE']:.2f} | {metrics['MAPE']:.2f} | {metrics['R2']:.4f} |\n"
    
    best_model = min(
        results.keys(), 
        key=lambda x: results[x]['metrics'].get('RMSE', float('inf')) if results[x]['metrics'] else float('inf')
    )
    
    table += f"\n### 🏆 Best Model: **{best_model}** (Lowest RMSE)\n\n"
    
    table += "#### Model Characteristics:\n\n"
    table += "- **ARIMA**: Statistical model, captures linear trends and autocorrelation\n"
    table += "- **LSTM**: Deep learning model, captures complex non-linear patterns\n"
    table += "- **Prophet**: Hybrid model, robust to missing data and seasonality\n\n"
    
    table += "#### Evaluation Metrics:\n\n"
    table += "- **RMSE** (Root Mean Squared Error): Lower is better, penalizes large errors\n"
    table += "- **MAE** (Mean Absolute Error): Average prediction error in dollars\n"
    table += "- **MAPE** (Mean Absolute Percentage Error): Prediction error as percentage\n"
    table += "- **R²** (R-squared): Proportion of variance explained (closer to 1 is better)\n"
    
    return table

def format_prediction_summary(df, predictions, days_to_predict):
    """Format prediction summary"""
    current_price = df['Close'].iloc[-1]
    current_date = df['Date'].iloc[-1].strftime('%Y-%m-%d')
    
    summary = f"### 📈 AAPL Stock Price Predictions ({days_to_predict} days ahead)\n\n"
    summary += f"**Current Price** (as of {current_date}): **${current_price:.2f}**\n\n"
    summary += "| Model | Predicted Price | Change ($) | Change (%) |\n"
    summary += "|-------|----------------|------------|------------|\n"
    
    for model_name, preds in predictions.items():
        pred_price = preds[-1]
        change = pred_price - current_price
        pct_change = (change / current_price) * 100
        
        emoji = "📈" if change > 0 else "📉"
        summary += f"| **{model_name}** {emoji} | ${pred_price:.2f} | ${change:+.2f} | {pct_change:+.2f}% |\n"
    
    avg_prediction = np.mean([preds[-1] for preds in predictions.values()])
    avg_change = avg_prediction - current_price
    avg_pct_change = (avg_change / current_price) * 100
    
    summary += f"\n**Average Prediction**: ${avg_prediction:.2f} ({avg_pct_change:+.2f}%)\n\n"
    
    summary += "#### ⚠️ Disclaimer\n"
    summary += "*These predictions are for educational purposes only. Stock market predictions are inherently uncertain. "
    summary += "Past performance does not guarantee future results. Always consult with financial advisors before making investment decisions.*\n"
    
    return summary

# ============================================================================
# MAIN INTERFACE FUNCTIONS
# ============================================================================

def run_evaluation():
    """Run model evaluation"""
    try:
        df = load_stock_data()
        results, train_data, test_data = rolling_window_evaluation(df, test_size=90)
        
        plot = create_comparison_plot(results, train_data, test_data)
        metrics_table = format_metrics_table(results)
        
        return plot, metrics_table
    except Exception as e:
        error_msg = f"### ❌ Error\n\n{str(e)}\n\nPlease make sure AAPL_stock_data.csv is uploaded."
        return None, error_msg

def run_prediction(days_to_predict):
    """Run future prediction"""
    try:
        df = load_stock_data()
        predictions = predict_future(df, int(days_to_predict))
        
        plot = create_future_plot(df, predictions, int(days_to_predict))
        summary = format_prediction_summary(df, predictions, int(days_to_predict))
        
        return plot, summary
    except Exception as e:
        error_msg = f"### ❌ Error\n\n{str(e)}\n\nPlease make sure AAPL_stock_data.csv is uploaded."
        return None, error_msg

# ============================================================================
# GRADIO INTERFACE
# ============================================================================

custom_css = """
.gradio-container {
    font-family: 'Arial', sans-serif;
}
.main-header {
    text-align: center;
    padding: 20px;
    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
    color: white;
    border-radius: 10px;
    margin-bottom: 20px;
}
"""

with gr.Blocks(title="Stock Price Forecasting - DataSynthis_ML_JobTask", theme=gr.themes.Soft(), css=custom_css) as demo:
    
    gr.Markdown("""
    <div class="main-header">
        <h1>📈 AAPL Stock Price Time-Series Forecasting</h1>
        <h3>DataSynthis ML Job Task - SET B</h3>
    </div>
    """)
    
    gr.Markdown("""
    This application implements and compares **three time-series forecasting models** for Apple Inc. (AAPL) stock price prediction:
    
    - 🔴 **ARIMA** (AutoRegressive Integrated Moving Average) - Traditional statistical model
    - 🟠 **LSTM** (Long Short-Term Memory) - Deep learning neural network
    - 🟣 **Prophet** - Facebook's robust forecasting tool
    
    ---
    """)
    
    with gr.Tabs() as tabs:
        
        # TAB 1: Exploratory Data Analysis
        with gr.Tab("📊 Exploratory Data Analysis"):
            gr.Markdown("""
            ### Dataset Overview & Statistical Analysis
            
            Explore the AAPL stock price data with comprehensive visualizations and statistical summaries.
            """)
            
            eda_btn = gr.Button("🔍 Generate EDA Report", variant="primary", size="lg")
            
            eda_plot = gr.Plot(label="EDA Visualizations")
            
            with gr.Row():
                with gr.Column():
                    eda_stats = gr.Markdown()
                with gr.Column():
                    eda_stationarity = gr.Markdown()
            
            eda_btn.click(
                fn=generate_eda,
                inputs=[],
                outputs=[eda_plot, eda_stats, eda_stationarity]
            )
        
        # TAB 2: Future Prediction
        with gr.Tab("🔮 Future Prediction"):
            gr.Markdown("""
            ### Predict Future Stock Prices
            Select the number of days you want to predict and click **Generate Predictions** to see forecasts from all three models.
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    days_slider = gr.Slider(
                        minimum=7,
                        maximum=90,
                        value=30,
                        step=1,
                        label="📅 Days to Predict",
                        info="Choose between 7 and 90 days"
                    )
                    predict_btn = gr.Button("🚀 Generate Predictions", variant="primary", size="lg")
                    
                    gr.Markdown("#### Quick Options:")
                    
                    with gr.Row():
                        btn_7 = gr.Button("1 Week", size="sm")
                        btn_30 = gr.Button("1 Month", size="sm")
                        btn_60 = gr.Button("2 Months", size="sm")
                        btn_90 = gr.Button("3 Months", size="sm")
                    
                    btn_7.click(lambda: 7, outputs=days_slider)
                    btn_30.click(lambda: 30, outputs=days_slider)
                    btn_60.click(lambda: 60, outputs=days_slider)
                    btn_90.click(lambda: 90, outputs=days_slider)
            
            prediction_plot = gr.Plot(label="📊 Forecast Chart")
            prediction_summary = gr.Markdown()
            
            predict_btn.click(
                fn=run_prediction,
                inputs=[days_slider],
                outputs=[prediction_plot, prediction_summary]
            )
        
        # TAB 3: Model Evaluation
        with gr.Tab("📊 Model Evaluation"):
            gr.Markdown("""
            ### Rolling Window Evaluation
            
            This evaluation uses the **last 90 days** as test data to compare model performance.
            
            **Evaluation Metrics:**
            - **RMSE**: Root Mean Squared Error (lower is better)
            - **MAE**: Mean Absolute Error (lower is better)
            - **MAPE**: Mean Absolute Percentage Error (lower is better)
            - **R²**: R-squared score (closer to 1 is better)
            
            Click the button below to run the evaluation:
            """)
            
            eval_btn = gr.Button("🔬 Run Evaluation", variant="primary", size="lg")
            
            evaluation_plot = gr.Plot(label="📊 Model Comparison Chart")
            metrics_output = gr.Markdown()
            
            eval_btn.click(
                fn=run_evaluation,
                inputs=[],
                outputs=[evaluation_plot, metrics_output]
            )
        
              # TAB 3: About
        with gr.Tab("ℹ️ About"):
            gr.Markdown("""
            ## 📝 Project Information
            
            ### Models Implemented
            
            #### 1️⃣ ARIMA (5,1,0)
            - **Type**: Statistical time-series model
            - **Strengths**: Fast, interpretable, good for linear trends
            - **Use Case**: Short-term predictions, linear patterns
            - **Training Time**: < 1 second
            
            #### 2️⃣ LSTM Neural Network
            - **Type**: Deep learning recurrent neural network
            - **Architecture**: 2 LSTM layers (50 units each) with dropout
            - **Strengths**: Captures complex non-linear patterns
            - **Use Case**: Medium to long-term predictions
            - **Training Time**: ~30-60 seconds
            
            #### 3️⃣ Prophet
            - **Type**: Additive regression model by Facebook
            - **Strengths**: Handles seasonality, robust to missing data
            - **Use Case**: Business forecasting, interpretable components
            - **Training Time**: ~5-10 seconds
            
            ---
            
            ### Dataset Information
            - **Stock**: Apple Inc. (AAPL)
            - **Features**: Date, Open, High, Low, Close, Volume
            - **Target Variable**: Close Price
            - **Data Source**: Historical stock data (CSV)
            
            ---
            
            ### Technical Stack
            - **Framework**: Gradio 4.44.0
            - **Deep Learning**: TensorFlow 2.15.0 / Keras
            - **Statistical Models**: Statsmodels 0.14.0
            - **Forecasting**: Prophet 1.1.5
            - **Data Processing**: Pandas, NumPy, Scikit-learn
            - **Visualization**: Plotly
            
            ---
            
            ### Deployment
            - **Platform**: Hugging Face Spaces
            - **Space Name**: DataSynthis_ML_JobTask
            
            
      
            
            ---
            
            ### 👨‍💻 Author
            **Md. Sadat Ahmed Rafi**  
            Task: Time-Series Forecasting – Stock Prices
          
            
            ---
            
            ### 📄 License
            This project is created for the DataSynthis ML Job Task evaluation.
            
            """)
    
    gr.Markdown("""
    ---
    <div style="text-align: center; color: #666; padding: 20px;">
        <p><strong>DataSynthis ML Job Task - SET B</strong></p>
        <p>Time-Series Forecasting – Stock Prices</p>
        <p>Deployed on Hugging Face Spaces | Powered by Gradio</p>
    </div>
    """)

# ============================================================================
# LAUNCH
# ============================================================================

if __name__ == "__main__":
    demo.launch()